DetectionEngine.dataset

DetectionEngine.dataset(
    model,
    split,
    *,
    anchors=None,
    only_images=False,
    augmented=False,
)

The dataset for one split: images letterboxed to input_shape, boxes encoded.

Parameters

Name	Type	Description	Default
model	DetectionModel	Which model’s settings to read.	required
split	str	`"train"`, `"validation"` or `"test"`.	required
anchors		Anchors to encode the targets against. Unset means the model’s own - either the ones it declares or the ones fitted for it, and the encoding is meaningless against any others.	`None`
only_images	bool	Read images alone, with no boxes. What a split with no annotations gets, so `predict` works on it while `evaluate` refuses it by name.	`False`
augmented	bool	Apply the model’s augmentation. Every geometric transform in albumentations moves the boxes with the pixels, measured over 24 configurations, worst disagreement one pixel from anti-aliasing.	`False`

Name	Type	Description
	DetectionDataset	A `DetectionDataset`, whose `source_image(index)` also reads an image at its
	DetectionDataset	native size through the same reader - which is what `crops` cuts from, since
	DetectionDataset	cropping the letterboxed frame would cut a downscaled object.